Why This Role Stands Out
This remote Agentic AI Engineer role offers a fantastic opportunity to shape innovative AI solutions for leading clients, leveraging your expertise in machine learning, NLP, and cloud platforms like Google Cloud. You'll thrive here if you're a proactive and skilled engineer eager to contribute to cutting-edge projects within a fast-growing, reputable firm.
Quick Overview
Seniority
Mid Senior
Work mode
Remote
Location
GA, United States
Posted
22 hours ago
DockerSQLMLOpsMachine LearningNLPAgileAzureBigQueryGoogle CloudJavaKerasKubernetesPyTorchPythonTensorFlow
Job Description
Stellar Consulting Solutions is a boutique business & technology consulting company headquartered in Atlanta, GA. We deliver high quality, agile, and experienced workforce for niche technology projects of any scale. We help forward thinking clients to solve specific problems by understanding their needs and align talent that can move fluidly
to match skill supply and demand on a real-time basis.
to match skill supply and demand on a real-time basis.
Stellar Consulting has a unique combination of technical and digital skills to recruit, engage, and retain qualified talent. We have a stellar reputation for striving to achieve high ethical standards. Our use of Innovative techniques and industry best practices has made us one of the fastest growing boutique firms delivering to enterprise business.
Title: Machine Learning Engineer (Agentic AI Engineer)
Location: Remote (PDT working hours)
Duration: 6-month contract (extension possible)
Location: Remote (PDT working hours)
Duration: 6-month contract (extension possible)
Job Description:
Proficiency in supervised and unsupervised learning algorithms is essential, along with experience in neural networks and natural language processing (NLP). Expertise in Python, R, and SQL is required, as well as familiarity with machine learning frameworks such as TensorFlow, Keras, and PyTorch. Knowledge of cloud platforms and technologies, specifically Microsoft Azure, is crucial. Experience in DevOps and MLOps practices is also necessary to streamline model deployment and management processes. This role involves leveraging these skills to develop, deploy, and optimize machine learning models for various applications.
Proficiency in supervised and unsupervised learning algorithms is essential, along with experience in neural networks and natural language processing (NLP). Expertise in Python, R, and SQL is required, as well as familiarity with machine learning frameworks such as TensorFlow, Keras, and PyTorch. Knowledge of cloud platforms and technologies, specifically Microsoft Azure, is crucial. Experience in DevOps and MLOps practices is also necessary to streamline model deployment and management processes. This role involves leveraging these skills to develop, deploy, and optimize machine learning models for various applications.
Mandatory Skills
- Google Cloud Platform (Google Cloud Platform) Mandatory and top priority
- Strong hands-on experience with Python
- Basic understanding of APIs
- Experience with BigQuery
- Exposure to AI SDKs
- Hands-on experience with LLMs, RAG (Retrieval-Augmented Generation), vector databases, and memory layers
- Exposure to Docker, GKE (Google Kubernetes Engine), and CI/CD
- AlloyDB experience is a plus
Nice-to-Have Skills
- Strong Java experience
- Experience with cross-cloud integration (Azure to Google Cloud Platform)
- Machine Learning knowledge is beneficial; however, the immediate focus is on building AI agents, not developing ML models.
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